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Technology · head to head

Apache Hadoop vs Sentry

Apache Hadoop logo

Apache Hadoop

Technology

The original open source framework for distributed storage and batch processing on commodity servers, now largely a legacy platform.

From
Free
Rated
-
Full reviewWebsite
Sentry logo

Sentry

Technology

Application monitoring platform built by developers for developers

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: Apache Hadoop the free vendor distributions no longer exist: Cloudera's CDH and Hortonworks' HDP have reached end of support and the successor CDP is subscription-only, so running Hadoop without paying now means assembling, testing and security-patching Apache releases yourself.; Sentry spending caps stop event ingestion when reached, eliminating visibility during critical moments
  • They diverge on capability: Apache Hadoop covers HDFS, Sentry covers Error tracking.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Apache Hadoop and Sentry actually diverge.

Attributes where Apache Hadoop and Sentry differ
AttributeApache HadoopSentry
Pricing modelopen-sourceUnknown
PlatformsWebWeb, iOS, Android, React Native, Desktop, 30+ frameworks and languages
FoundedUnknown2011

Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Technology).

What each one covers

Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.

Only in Apache Hadoop

  • HDFS
  • YARN
  • MapReduce
  • HDFS federation and high availability
  • Kerberos security
  • Rack awareness
  • S3A and object store connectors
  • Ecosystem compatibility

Only in Sentry

  • Error tracking
  • Performance monitoring
  • Release tracking
  • Real user monitoring
  • Alerting
  • Issue assignment
  • Breadcrumbs
  • Source maps

What people use each for

The jobs each tool is most often brought in to do.

Apache Hadoop

  • Operating an existing multi-petabyte on-premises estate where data residency or egress costs rule out moving to cloud object storagenot Sentry
  • Running Spark or Flink under YARN on hardware you already own, using HDFS as the storage layernot Sentry
  • Keeping long-lived regulated archives on infrastructure entirely within your own data centres and legal jurisdictionnot Sentry
  • Maintaining legacy Hive and MapReduce workloads during a staged migration to a lakehouse or cloud platformnot Sentry

Sentry

  • Error monitoringnot Apache Hadoop
  • Performance trackingnot Apache Hadoop
  • Debug production issuesnot Apache Hadoop
  • Release managementnot Apache Hadoop
  • User monitoringnot Apache Hadoop

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

Apache Hadoop

  • The free vendor distributions no longer exist: Cloudera's CDH and Hortonworks' HDP have reached end of support and the successor CDP is subscription-only, so running Hadoop without paying now means assembling, testing and security-patching Apache releases yourself.
  • HDFS couples storage to compute, so adding capacity means buying whole nodes with CPU and memory you may not need, and the entire industry moved to object storage precisely because it lets the two be bought separately.
  • The NameNode holds all filesystem metadata in memory, so a cluster with tens of millions of small files exhausts heap long before it exhausts disk, and the remedy is a file compaction job that somebody has to write, schedule and own indefinitely.
  • Operating it is a distinct specialism covering Kerberos, YARN queue tuning, JVM garbage collection and the compatibility matrix between Hive, HBase, Ranger, Oozie and the core, and an upgrade touches all of them at once rather than one at a time.
  • MapReduce is maintained for compatibility rather than actively developed, and new work goes to Spark or Flink, so a job written against MapReduce today is written against an API that will not gain anything further.
  • Hiring is against you: the talent pool has moved to cloud data platforms over the past decade, so a Hadoop estate increasingly depends on a small number of individuals, which makes it a succession risk before it is a technical one.

Sentry

  • Spending caps stop event ingestion when reached, eliminating visibility during critical moments
  • Complex configuration required for filters, sampling rules, issue grouping, and alert policies
  • Difficult to configure custom alerts and alert content without creating email inbox bloat
  • Error grouping is imperfect with noise and filtering issues causing incorrect error prioritization
  • Weak for distributed tracing across microservices compared to dedicated APM tools
  • UI dashboard is less customizable than alternatives like Datadog APM for complex monitoring needs

Pricing, plan by plan

Apache Hadoop

Free

No published plan breakdown. See the Apache Hadoop review.

Sentry

Free
  • Developer (Free)Free
    • 5K errors per month
    • 1 user seat
    • 30-day data retention
  • Team$26/month
    • 50K errors per month
    • 5M transaction spans
    • 90-day data retention
  • Business$80/month
    • Higher quotas
    • Extended retention
    • Advanced filtering
  • Organization$199/month
    • SSO integration
    • Audit logs
    • Advanced security

Which should you pick?

Choose Apache Hadoop if

  • You need hdfs.
  • You want to start without paying.
  • You also want yarn.
Apache Hadoop review

Choose Sentry if

  • You need error tracking.
  • You want to start without paying.
  • You work on Web, iOS, Android, React Native, Desktop, 30+ frameworks and languages.
  • You also want performance monitoring.
Sentry review

Questions people ask

Is Apache Hadoop or Sentry better?
Neither clearly leads. Apache Hadoop starts at Free and Sentry at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Hadoop or Sentry?
Apache Hadoop starts at Free and Sentry at Free.
Does Apache Hadoop or Sentry run on more platforms?
Apache Hadoop runs on Web. Sentry runs on Web, iOS, Android, React Native, Desktop, 30+ frameworks and languages.
Can I use Apache Hadoop for free?
Both have a free tier, so you can try either at no cost before committing.
What is Apache Hadoop best used for?
Apache Hadoop is most often used for operating an existing multi-petabyte on-premises estate where data residency or egress costs rule out moving to cloud object storage, running spark or flink under yarn on hardware you already own, using hdfs as the storage layer, keeping long-lived regulated archives on infrastructure entirely within your own data centres and legal jurisdiction, maintaining legacy hive and mapreduce workloads during a staged migration to a lakehouse or cloud platform. Of those, operating an existing multi-petabyte on-premises estate where data residency or egress costs rule out moving to cloud object storage and running spark or flink under yarn on hardware you already own, using hdfs as the storage layer are not what Sentry is typically brought in for.
What can Apache Hadoop do that Sentry cannot?
Apache Hadoop covers HDFS, YARN, MapReduce, HDFS federation and high availability. Sentry covers Error tracking, Performance monitoring, Release tracking, Real user monitoring.

Answered from the vendors’ own pages

Apache Hadoop: Is Hadoop dead?

No, but it is legacy. Large on-premises HDFS estates still run and are still supported, and Spark and Flink still run on YARN. What has ended is Hadoop as a default choice for new platforms, which now start on object storage.

Sentry: Does Sentry have a free tier?

Yes. The free Developer tier includes 5,000 errors per month, one user seat, 30-day retention, and 50 session replays per month.

Source
Apache Hadoop: Can I still get a free packaged distribution?

Not a maintained one. CDH and HDP reached end of support and Cloudera's CDP is a paid subscription. The remaining free route is building and patching Apache releases yourself, which is a real engineering commitment.

Sentry: How much do Sentry's paid plans cost?

Team plan starts at $26/month (annual) or $29/month (monthly) with 50K errors and 5M spans included. Business plan is $80-89/month. Organization plans start at $199/month with SSO and advanced compliance features.

Source
Apache Hadoop: Do I need Hadoop to run Spark?

No. Spark runs standalone, on Kubernetes and on managed cloud services, and reads object storage directly. Many Spark deployments include Hadoop client libraries for the filesystem connectors without running a Hadoop cluster at all.

Sentry: What programming languages does Sentry support?

Sentry supports over 30 languages and frameworks including JavaScript, Python, Go, Ruby, Java, .NET, PHP, Node.js, and mobile platforms including iOS, Android, and React Native.

Source
Apache Hadoop: What replaced HDFS?

Object storage, typically S3 or a compatible system, combined with an open table format such as Apache Iceberg or Delta Lake. Apache Ozone exists as an object store within the Hadoop ecosystem for organisations staying on-premises.

Sentry: Does Sentry support self-hosting?

Yes. Sentry can be self-hosted, and the open-source version is available for deployment in on-premises environments.

Source
Apache Hadoop: Is it cheaper than the cloud?

It can be at multi-petabyte scale with steady, predictable utilisation, particularly where egress charges would be large. Include the staffing cost honestly, because the specialist operators a Hadoop cluster requires are scarce and therefore expensive.

Sentry: How does Sentry billing work if I exceed my quota?

Sentry offers spending caps that stop ingestion when reached, meaning you lose visibility exactly when you need it most. You can pre-purchase reserved capacity at 20% discount or pay per event on-demand when exceeding included allotment.

Source
Sentry: What integrations does Sentry support?

Sentry integrates with GitHub, GitLab, Jira, Slack, PagerDuty, most CI/CD pipelines, and many other developer tools.

Source
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